A customary method for presetting the parameters of neural networks is through initialization with random numbers and subsequent optimization. The disadvantages associated with this method are, however, that the neural networks that are obtained are not reproducible, and they do not always produce meaningful results when there is a substantially uneven data distribution. For that reason, even with the same training data, repeated calculations of the network parameters can lead each time to different parameter sets. In this context, it is difficult to compare the results to one another. When the parameters change, it cannot be determined on the basis of the non-reproducible results whether these changes are caused solely by changed training data.
PCT Published Application No. WO 94/06095 describes a device for designing a neural network that is capable of producing reproducible results for training data records. In this context, the parameters of the neural network are calculated by solving a linear system of equations. The design process can be roughly divided into two steps: first, equally distributed auxiliary quantities are introduced onto the domain of the input signals to define the parameters of the neurons in the intermediate layer; the parameters of the output neurons are then each determined by solving a system of equations. When, to assure the local effectiveness of the neurons, a function having a characteristic bell-shaped curve is used for the non-linear elements in the neurons of the intermediate layer, the known device has the drawback that the number of neurons required in the intermediate layer is dependent on the number of auxiliary quantities, so that a large number of neurons is required for a neural network that has satisfactory interpolation properties.